Languages
Go Development Company
The language of the cloud, in the hands of engineers who operate what they ship.
Overview
Go was designed at Google to solve a specific organisational problem, not a theoretical one: large teams building networked services who were tired of slow builds, tangled dependency graphs, and the ceremony of C++ and Java. The result is a language that is deliberately, almost aggressively small. You can read the entire specification in an afternoon, and most competent engineers are writing useful code within a week. That restraint is the whole point. Go trades expressive power for a codebase that reads the same whether it was written by a junior six months ago or a principal engineer last Tuesday, and on a system that has to survive staff turnover, that trade is worth far more than it costs.
What makes Go worth reaching for is the combination sitting underneath that simplicity. Goroutines make concurrency approachable rather than terrifying, so a service holding tens of thousands of simultaneous connections is written as ordinary sequential code rather than as a callback maze. The compiler produces a single static binary with no runtime to install, so deployment is a file copy. Startup is effectively instant because there is no JIT to warm and no interpreter to boot, and the memory footprint of a real service is measured in low tens of megabytes rather than hundreds. That set of properties is exactly why Docker, Kubernetes, Terraform, Prometheus, etcd and a large share of the modern cloud control plane are written in Go. When you deploy that infrastructure, you are already deploying Go, whether or not anyone on your team writes it.
The language has also matured in the places it used to hurt. Modules replaced the old GOPATH awkwardness and gave the ecosystem reproducible builds with a checksum database behind them. Generics landed in Go 1.18 and, while still deliberately narrower than what Rust or TypeScript offer, they removed the worst of the copy-paste-per-type drudgery. Structured logging arrived in the standard library. The compatibility promise means code written years ago still builds against a current toolchain, which is an underrated property when you are choosing what to bet a platform on for the next decade.
At Yarqat we use Go where its strengths are decisive: networked services, API and gRPC backends, ingestion and streaming pipelines, command-line tooling, Kubernetes operators and infrastructure glue. We are equally candid about where it is a poor fit, and we say so before a contract rather than after. This page is written by engineers who have carried Go services through three-in-the-morning incidents, profiled them with pprof while a queue backed up, and chased down a leaked goroutine that only appeared under real traffic. It is not a language brochure.
Best for: High-throughput backend services, cloud-native microservices, APIs, ingestion pipelines, CLIs and infrastructure tooling, where predictable performance, easy concurrency and effortless deployment matter more than a maximal ecosystem or expressive syntax.
Why teams choose Go
Concurrency without the fear
Goroutines and channels let you model thousands of concurrent operations as ordinary sequential code. The runtime multiplexes them onto a small pool of OS threads and parks anything blocked on I/O, so a service handling an enormous number of open connections stays readable instead of collapsing into callback spaghetti or a thread pool you have to tune by hand.
Deploy a single file
go build produces one static binary with no external runtime. That means a container image measured in single-digit megabytes, cold starts fast enough to be irrelevant, and none of the "works on my machine" friction that comes from interpreter and dependency version drift between a developer laptop and production.
Cost that scales the right way
Low memory use, fast startup and native compilation mean you run fewer and smaller instances for the same offered load. Teams moving hot-path services off heavier runtimes generally find compute per request drops, and because Go instances start in milliseconds you can scale down aggressively instead of paying to keep warm capacity idle.
A codebase that survives its authors
gofmt gives one canonical format, the language gives one obvious way to do most things, and the compatibility promise means old code keeps building. The practical effect is that a Go service written three years ago by people who have since left is still approachable, which is precisely the property that decides total cost of ownership.
Why businesses choose Go
- You need the raw efficiency of a compiled language but want development speed closer to a scripting language, without the ramp-up cost of Rust.
- Your platform lives in containers and Kubernetes, and you want your services speaking the same native language as the infrastructure and its client libraries.
- You are optimising for a service that stays legible and cheap to run for years, rather than one that showcases the newest language paradigm.
- You want a codebase where engineers can read each other’s work without a style war, because gofmt and the community conventions end most of those arguments before they start.
- You are consolidating a sprawl of services onto fewer, larger instances, and want per-request cost to fall without rewriting your operational model.
What we build with Go
The capabilities this technology is genuinely strong at, and what we most often build with it.
Goroutines
Lightweight, runtime-scheduled routines that start with a small stack that grows on demand. You can spawn very large numbers of them where OS threads would exhaust memory, which makes massively concurrent servers practical on modest hardware and makes per-connection code straightforward to write.
Channels and select
Typed pipes for passing data and coordinating goroutines, following the maxim "share memory by communicating". The select statement composes multiple channels, timeouts and cancellation into one readable block, which is where a lot of otherwise gnarly asynchronous coordination becomes tractable.
Context propagation
context.Context carries deadlines, cancellation and request-scoped values down the call tree, so abandoning a request tears down the work it started instead of leaving goroutines and database queries running. Used consistently it is the single most effective defence against resource leaks in a Go service.
Static single binary and cross-compilation
Cross-compile for any target from one machine: set GOOS and GOARCH and you have a binary for a different operating system or an arm64 instance. No runtime, no shared libraries, no surprises on the deployment host, and a build pipeline that does not need the target platform to produce artefacts for it.
A batteries-included standard library
A production-grade HTTP server and client, TLS, JSON, crypto, templating, structured logging and a testing framework all ship in the box. Many serious Go services depend on almost no third-party packages for their core behaviour, which is both a security posture and a maintenance strategy.
Interfaces satisfied implicitly
A type implements an interface simply by having the right methods, with no declaration of intent. That inverts dependency direction naturally: consumers define the narrow interface they need, which makes testing with fakes trivial and keeps packages from growing hard couplings to one another.
Built-in tooling that ships with the language
gofmt enforces one canonical format, go test runs tests and benchmarks, go vet and the race detector catch whole classes of bug before production, pprof profiles CPU, memory, blocking and mutex contention, and govulncheck cross-references known vulnerabilities against the code paths you actually call.
Garbage collection tuned for latency
Go’s concurrent collector targets very short stop-the-world pauses, so services stay responsive under load without the tuning rituals that older collected runtimes demand. Where it does show up, GOGC and the soft memory limit give you honest levers rather than a wall of flags.
Use cases
API and microservice backends
REST and gRPC services that must hold many concurrent connections at tight, predictable latency. Go’s net/http, the gRPC ecosystem and the sheer cheapness of a goroutine per request make this the language’s natural territory, and the operational simplicity of a single binary keeps the deployment story small as service counts grow.
Cloud-native infrastructure and platform tooling
Kubernetes operators, controllers, admission webhooks, custom schedulers and internal platform CLIs. The Kubernetes API machinery, client-go and the code generators are Go-native, so extending your platform in Go is working with the grain rather than against it.
High-throughput networking, ingestion and streaming
Proxies, API gateways, event ingestion endpoints, Kafka consumers and real-time pipelines where you are pushing very large volumes of small messages and cannot afford per-request overhead. Backpressure, batching and worker pools are natural to express with channels and contexts.
Command-line tools and developer tooling
Fast-starting single-binary CLIs that ship to macOS, Linux and Windows from one build pipeline. Distribution is a file copy with no runtime prerequisite, which is exactly why so much of the modern DevOps toolchain chose Go and why internal tooling teams keep making the same choice.
Replacing a hot path inside a larger system
A single latency-critical or cost-critical service extracted from a Python or Node monolith and rewritten in Go, behind the same interface. This is a low-risk way to get most of the benefit without a rewrite, and it is one of the most common Go engagements we take on.
Scheduled jobs, workers and batch processing
Queue consumers, cron-style workers and data reconciliation jobs where instant startup and small memory use mean you can run them densely, scale them to zero between runs, and stop paying for capacity that spends most of its life idle.
When Go is the right choice
- Right when you are building HTTP or gRPC services that need to hold high concurrency with predictable latency inside a small resource budget. This is Go’s home ground and it is very hard to beat there.
- Right when you want deployment to be genuinely trivial: a single static binary copied into a scratch or distroless container, with no interpreter on the host, no dependency resolution at deploy time, and an image small enough that pulls are not a scheduling delay.
- Right when your platform lives in containers and Kubernetes. Writing operators, controllers, admission webhooks and CLIs in Go removes an entire layer of impedance mismatch, because the client libraries, the code generators and the API machinery are all native Go and first-class.
- Right when CPU and memory efficiency translate directly into cost. Go often does the work of a considerably larger JVM or Node fleet on the same hardware, and because startup is instant it fits autoscaling and spot capacity in a way heavier runtimes do not.
- Right when the team values long-term maintainability and onboarding speed over cutting-edge language features. Go’s uniformity means code review is about behaviour rather than style, and a new engineer becomes useful quickly.
- Wrong for data science, machine learning training pipelines and heavy numerical work. Python owns that ecosystem completely and you will spend the project reimplementing libraries that already exist and are better than what you will write. We would reach for Python there, and expose the result to Go over an API if the serving layer needs Go’s throughput.
- Wrong for rich desktop or mobile user interfaces. The GUI tooling is immature, the bindings are thin, and you will fight the ecosystem the whole way. Use the platform-native stack, or Flutter, or a web front end.
- Wrong for domains that live inside deep, mature Java or .NET libraries, where choosing Go means rewriting decades of well-tested integration code. And wrong when every microsecond and every byte genuinely matter at the hardware level: a garbage-collected runtime is the wrong shape for that, and Rust or C is the honest answer.
Go: pros and cons
Strengths
- An excellent, built-in concurrency model that ordinary engineers can use correctly, rather than a threading library only the resident expert understands.
- Compilation fast enough to keep the edit, build, test loop tight even on a large codebase, which changes how a team works day to day more than most people expect.
- Static binaries and a genuinely production-grade standard library make deployment and networking almost boring, in the best possible sense.
- A small language surface keeps codebases uniform, shortens onboarding, and removes most of the stylistic arguments that consume code review elsewhere.
- First-class built-in tooling: the race detector, pprof profiling, go vet, benchmarking and test coverage all ship with the toolchain, so diagnosing a production problem does not start with choosing a tool.
- A strong compatibility promise and a well-run module system with a checksum database, so upgrades are usually uneventful and supply-chain provenance is verifiable.
Trade-offs
- Error handling is verbose, and no amount of advocacy makes it otherwise. The repeated if err != nil block is honest and explicit, but it is noise, it pads functions out, and it makes some code harder to skim than an exception-based equivalent would be.
- Generics arrived late, in Go 1.18, and remain deliberately narrower than in Rust or TypeScript. There is no way to parameterise on methods in the way people expect, type inference gives up in places you would not predict, and certain abstractions stay awkward or fall back to interface{} and reflection.
- The deliberate minimalism frustrates engineers coming from richer languages. There is no ternary operator, no sum types or exhaustive enums, no operator overloading, no proper immutability, and limited metaprogramming. Constants and iota are a poor substitute for real algebraic types, and the compiler will not tell you when you have missed a case in a switch.
- The ecosystem is strong for infrastructure, networking and services, and noticeably thinner elsewhere. Data science, GUI, scientific computing and certain enterprise integrations either lack libraries or have one unmaintained option, and you will end up writing what you would otherwise have imported.
- The nil interface trap is a real and recurring source of production bugs: an interface holding a typed nil pointer is not equal to nil, which surprises everyone at least once and usually in the worst possible place.
- The garbage collector is tuned for low pause times rather than maximum throughput, so allocation-heavy code spends real CPU on collection. For almost all services this is invisible. For sub-millisecond, allocation-sensitive hot paths it is a ceiling, and that is a genuine reason to choose Rust instead.
- Struct tags and reflection-driven encoding are stringly typed and unchecked by the compiler, so a mistyped JSON tag is a runtime surprise rather than a build failure.
The concurrency model, and why it holds up
Go’s concurrency rests on CSP, communicating sequential processes. Instead of threads sharing mutable state guarded by locks, you run goroutines that pass ownership of data through channels. A goroutine is not an OS thread: the runtime scheduler multiplexes many goroutines onto a small pool of threads, parking any that block on I/O and running another in its place. That is why a Go server can hold an enormous number of mostly-idle connections cheaply. Each one is a goroutine waiting on a channel or a socket, costing a small stack rather than a full thread.
In practice we lean on a few disciplines to keep this safe, because the model is easy to use and also easy to misuse. Every goroutine has an identified owner and a defined exit: something starts it, something can stop it, and nothing is left running when the work that justified it is gone. A context.Context carries cancellation and deadlines down the entire call tree so an abandoned request does not leave a database query and three HTTP calls still in flight. We bound concurrency deliberately with worker pools or semaphores rather than spawning a goroutine per unit of unbounded input, because the fastest way to turn a Go service into a memory incident is to let an upstream queue decide how many goroutines you run.
Structurally, we design services with dependencies pointing inward: transport at the edge, domain logic in the middle with no knowledge of HTTP or the database, and adapters at the boundary. Interfaces are defined by consumers and kept narrow, which is what makes Go testing genuinely pleasant rather than a mocking framework exercise. We run the race detector in CI because unsynchronised access is silent until it is catastrophic, and we prefer channels for coordination without being dogmatic: a plain sync.Mutex around a small piece of shared state is often clearer than a channel dance, and pretending otherwise produces worse code. Done well, the model gives concurrency that is both fast and readable. Done carelessly it gives leaked goroutines and subtle races, which is exactly why senior review matters here more than the language’s reputation for simplicity suggests.
Performance characteristics
Go compiles to native machine code, so throughput for typical server workloads sits close to C++ and Rust and far above interpreted runtimes. Startup is effectively instant because there is no JIT warm-up phase, which makes Go excellent for autoscaling, spot capacity and short-lived workloads where a JVM would still be identifying its hot paths when the container is asked to shut down. Memory footprint is modest and, more usefully, predictable: a real service typically sits in tens of megabytes, and it does not drift upward in the way runtimes with large managed heaps sometimes do.
The honest caveat is the garbage collector. Go’s GC optimises for short pauses rather than peak throughput, so allocation-heavy code spends measurable CPU on collection and can show up as tail latency under sustained pressure. For the vast majority of services this never surfaces. When it does, the fixes are well understood: profile with pprof before changing anything, reduce allocations on the hot path by reusing buffers, avoid needless interface boxing and string concatenation in loops, preallocate slices when the size is known, and use sync.Pool where it genuinely earns its keep rather than as a reflex. GOGC and the soft memory limit give you a real trade between CPU spent collecting and resident memory, and they are worth setting deliberately rather than leaving at the default in a container with a hard limit.
Our approach is measurement first, always. Go’s benchmarking is part of the standard test tooling, so a performance claim can be checked in the same pull request that makes it, and pprof profiles for CPU, heap, blocking and mutex contention can be pulled from a live process over an authenticated endpoint. That combination means production performance work is diagnostic rather than speculative: you find the specific function, the specific allocation or the specific lock that is costing you, and you fix that. We have far more often found the bottleneck in an N+1 database pattern, an unindexed query or a serial fan-out that should have been parallel than in anything the language was doing.
Security posture
Go removes several entire classes of vulnerability by design. It is memory-safe, with bounds-checked slices, no pointer arithmetic and no manual free, so buffer overflows, use-after-free and double-free simply do not occur in ordinary code. That alone rules out a large share of the CVEs that historically plague network-facing services written in C and C++. The standard library ships modern, actively maintained TLS and cryptography, which keeps the security-critical parts of most services off the third-party dependency treadmill entirely, and a small dependency tree means a correspondingly small supply-chain attack surface.
We harden further using the tooling the ecosystem provides properly rather than performatively. govulncheck is the important one: it cross-references your module graph against the Go vulnerability database and then narrows the result to the code paths you actually call, so you are triaging real exposure rather than chasing advisories for functions your binary never reaches. Module checksums and the transparency log give verifiable provenance for dependencies. Static binaries run comfortably in distroless or scratch containers with no shell and no package manager, which sharply limits what an attacker can do after an initial foothold, and running as a non-root user with a read-only filesystem costs nothing and closes more doors.
The rest is ordinary engineering discipline applied consistently: input validated at the boundary and parsed into typed structures rather than passed around as maps, database access through parameterised queries with no string-built SQL, secrets read from the environment or a secret manager and never compiled into the binary or committed, sensible timeouts on every inbound and outbound HTTP call so a slow dependency cannot exhaust your connections, and least-privilege credentials for every integration. We also treat structured logging as a security concern, because the fastest way to leak personal data is to log a whole request struct for debugging and never remove it.
Scaling in production
Go scales cleanly in both directions, which is unusual. Vertically, goroutines let one instance saturate the available cores and hold very high connection counts without the per-thread memory tax, so you frequently get more out of a single machine than capacity planning based on other runtimes would suggest. Horizontally, small stateless binaries with instant startup are close to ideal for Kubernetes: pods schedule and become ready quickly, rolling deployments are fast because images are tiny, scale-to-zero is painless, and a horizontal pod autoscaler can respond to load in something like real time rather than after a warm-up period.
The architectural work is mostly about keeping instances stateless and pushing shared state into Postgres, Redis or the message broker, so the platform can add and remove capacity freely. From there the bottleneck almost always moves off the Go process and onto something else: connection pool limits at the database, partition counts in Kafka, or a downstream service that cannot absorb the concurrency you are now capable of generating. That is a good problem, but it has to be anticipated. We size connection pools deliberately against the database’s actual limits, apply bounded concurrency and backpressure so a Go service degrades gracefully instead of stampeding its dependencies, and add circuit breaking and timeouts at every boundary.
Because a Go binary starts in milliseconds and holds little memory, cost strategies that are clumsy with heavier runtimes become straightforward: aggressive scale-down out of hours, spot or preemptible capacity for workers, and dense packing of small services onto fewer nodes. We instrument from the first deployment with structured logs, metrics and distributed tracing, because scaling decisions made without measurement are guesses, and the point of choosing a runtime this efficient is to spend the saving deliberately rather than absorb it into over-provisioning nobody ever revisits.
Go integrations & ecosystem
The technologies we most often pair with it. Each links to how we work with it.
How we build Go systems
We start with the interfaces and the data flow, not the frameworks. Go rewards a design where packages have clear boundaries and dependencies point inward, so we sketch the service’s public surface, its data model and its failure modes before anyone writes a handler. Idiomatic Go is a real and specific thing, and we follow the community conventions and Effective Go rather than importing patterns from Java or Python, because fighting the language’s grain is where Go codebases reliably go wrong. We keep the dependency list short and deliberate, favouring the standard library, because every third-party package is a maintenance and supply-chain commitment that outlasts the sprint that introduced it.
From there it is tight, tested iteration. Table-driven tests cover behaviour and make edge cases cheap to add, the race detector runs in CI on every change, go vet and a linter enforce the conventions so review is about design rather than formatting, and anything on a hot path is profiled before it is optimised rather than tuned on a hunch. We ship early behind observability, with structured logging, metrics and tracing wired in from the first deployment, so the first production incident is diagnosable in minutes rather than being a mystery reconstructed from grep.
We also treat handover as part of the work, not an afterthought at the end. The codebase you get is formatted canonically, documented where the reasoning is not obvious from the code, buildable from a clean checkout with one command, and shipped with the dashboards, alerts and runbooks needed to operate it. Where we are working alongside your engineers, we pair and review deliberately on the idioms that trip people up coming from other languages: error wrapping, goroutine ownership, context propagation and interface design. The goal is that your team can own the service confidently, not that they need us to keep it running.
The service behind it
Delivered throughCustom Software DevelopmentWhat we build with Go
The disciplines this technology most often shows up in, from a first build to taking over and stabilising an existing one.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves, including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
Weighing up Go?
A short call with engineers who build in it and operate the result. If Go is the wrong tool for what you are doing, we would rather tell you now than bill you later.
Industries we use Go in
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
Also in Languages
Why teams choose us for Go
We operate what we ship
Yarqat runs its own products in production on the same stacks we recommend. Our Go advice comes from carrying pagers, profiling live processes and fixing things at unsociable hours, not from a slide deck about language benchmarks.
Senior engineers, no bench-warming
The people who scope your work are the people who write it. You are not funding a layer of juniors learning goroutine lifecycles and context propagation on your budget while a delivery manager relays questions.
Honest about fit
If Go is the wrong tool for your problem we will tell you and suggest what is right, whether that is Python for the data work, Rust for the truly latency-critical component, or simply keeping what you already have. We would rather lose a project than saddle you with a mismatch we have to defend later.
Rescue and modernisation, not just greenfield
A good deal of our Go work is inheriting a service that leaks goroutines, exhausts its database pool, or falls over under a load it was never tested against. Diagnosing that needs someone comfortable reading a heap profile and a query plan in the same afternoon.
Registered and accountable
A real UK-registered company you can hold to account, with clear contracts, and a reputation that depends on your systems still working long after we have stepped back.
Typical timeline
- 01
Discovery
One to two weeks defining service boundaries, the data model, throughput and latency targets, the integrations involved, and which risks are worth de-risking first. This is also where we confirm Go is genuinely the right choice rather than the assumed one.
- 02
Prototype
Two to four weeks to a working service running on real infrastructure, exercising the hardest path end to end rather than the easy demo, with CI, observability and deployment proven before the codebase grows around them.
- 03
Build
Iterative delivery in short cycles, each ending with something deployed, tested, observable, and exercised under load representative of production rather than of a developer laptop.
- 04
Harden
Load and soak testing, profiling the hot paths, tuning connection pools and concurrency limits, a security pass with govulncheck and dependency review, and the alerting and dashboards that make the service operable by someone who did not write it.
- 05
Operate or hand over
We run what we build, with monitoring, incident response and ongoing tuning, or we hand it over with the runbooks, dashboards and documented conventions your team needs to run it themselves. Both are fine. Lock-in is not part of the offer.
How pricing works
- Most engagements begin with a paid discovery phase. We map the service boundaries, the throughput and latency targets, the data model and the integrations before quoting the build, so the number reflects your actual requirements rather than an optimistic guess dressed up as an estimate.
- Well-understood pieces of work are quoted fixed-scope: a specific service, a migration of one hot path off an existing runtime, a CLI, or a Kubernetes operator with defined behaviour. We agree what "done" means, in writing, before we start.
- Longer programmes run as a monthly senior engagement: a defined team building and operating against agreed outcomes, reviewed on a regular cadence, with the option to scale up or stop without a penalty clause. We are equally happy working alongside your own engineers and handing over cleanly.
- The honest cost drivers are the number and complexity of integrations, the throughput and latency bar the system must clear, whether we are building fresh or stabilising something inherited, and the operational and regulatory standard the result has to meet. The language itself is rarely the expensive part.
- Third-party costs (cloud hosting, managed databases, message brokers, observability platforms, licences) are billed to your own accounts at cost, with no mark-up from us. You own the accounts, you see the invoices, and you keep them if we part company.
Hire Go engineers
Need Go capacity on your own team? We embed named senior engineers into your existing team (reporting to your leads, working in your rituals), so you add capacity without a hiring cycle.
Hire Go engineersCommon questions
Is Go a good choice if my team has never written it?
Usually yes, and this is one of the strongest practical arguments for the language. Go’s small surface means competent engineers coming from Java, C#, Python or Node are writing useful code within a week or two, and gofmt ends most style debates before anyone has them. The learning curve is not in the syntax but in the idioms: error wrapping, goroutine ownership, context propagation and designing narrow interfaces. That is exactly where we pair and review, so your team becomes genuinely fluent rather than writing Java with Go keywords.
How does Go compare to Rust for a backend service?
Rust gives you more raw performance, no garbage collector, and much stronger compile-time guarantees about memory and concurrency, at the cost of a steeper learning curve and slower day-to-day development. Go trades a little peak performance for considerably faster delivery and much easier hiring. For most networked services the throughput difference is irrelevant next to the difference in time to ship, and Go wins clearly. For a component where every microsecond and every byte counts, where GC pauses are unacceptable, or where you are writing something close to the hardware, Rust is the better call and we will say so.
Does the verbose error handling actually cause problems in practice?
It causes irritation more than problems. The if err != nil pattern is repetitive and it does pad functions out, but it forces you to confront every failure at the point it happens instead of letting an exception unwind silently through three layers to a generic handler. Wrapping errors with fmt.Errorf and %w gives you a clear failure chain, and errors.Is and errors.As let callers make decisions on specific failures rather than parsing strings. Most teams stop noticing the verbosity within a month and come to value the explicitness during incidents, which is when it pays for itself.
Is the late arrival of generics still a limitation?
Less than it was, but it is real. Generics landed in Go 1.18 and cover the common cases well: generic containers, constraints, and utility functions over slices and maps, which is why the standard library now has slices and maps packages. They remain deliberately narrower than in Rust or TypeScript, with no method-level type parameters and inference that gives up in places that surprise people. For typical service code you rarely hit the ceiling. If your design leans heavily on sophisticated type-level abstraction, Go will feel constraining and you should factor that in.
Will a Go rewrite actually reduce our cloud bill?
Often, but we will not put a number on it before measuring, and you should be sceptical of anyone who does. The gains come from lower memory per instance, native compilation, and startup fast enough to let you scale down aggressively instead of paying for warm capacity. Whether that translates into a materially smaller bill depends on whether compute is actually your cost centre. If your spend is dominated by the database, egress or a SaaS platform, changing language will not move it. We would rather measure your real workload during discovery and tell you the honest answer than sell a rewrite on a general claim.
Can we adopt Go incrementally instead of rewriting everything?
Yes, and it is almost always what we recommend. The usual approach is to extract one service, typically a hot path or a piece of infrastructure tooling, put it behind the same interface the rest of the system already calls, and run it alongside what exists. Go’s deployment story makes this genuinely low risk: a single binary in a small container, easy to roll back. You get evidence about performance, cost and how your team finds the language before anyone commits to a wider migration, and if the evidence is unconvincing you have lost one service, not a roadmap.
What breaks most often in Go services you are called in to fix?
Leaked goroutines that never exit because nothing cancels them, unbounded concurrency where a goroutine is spawned per item from an upstream queue, missing timeouts on outbound HTTP calls so a slow dependency exhausts the whole service, and database connection pools sized without reference to what the database will actually accept. Notably, almost none of these are language faults. They are design decisions the language happens to make very easy to get wrong quickly, which is why goroutine ownership and bounded concurrency are things we are strict about from the first commit.
Where would you specifically advise against using Go?
We steer away from Go for data science and machine learning, where Python’s ecosystem is unmatched and you would spend the project reimplementing libraries. We avoid it for rich desktop and mobile user interfaces, where the tooling is immature. It is not the natural choice for domains built on deep, mature Java or .NET libraries you would otherwise have to rewrite, nor for hard real-time work where a garbage collector is disqualifying. Go is superb for services, pipelines and infrastructure. It is not trying to be everything, and its designers would say the same.
Building on Go?
Tell us what you are building and where it is stuck. A senior engineer reads it and gives you an honest read on whether Go is the right fit for the problem, or what we would reach for instead.
- 01A senior engineer reads it. Not a form queue, and not an account manager.
- 02We reply either with questions or with a straight answer that we are not the right fit.
- 03If it looks like a fit, a technical call with the person who would actually run the delivery.
- 04Then scope, effort and risk in writing, before anyone signs anything.